DOI: https://doi.org/10.20998/2074-272X.2019.2.07

ON-LINE VOLTAGE STABILITY EVALUATION USING NEURO-FUZZY INFERENCE SYSTEM AND MOTH-FLAME OPTIMIZATION ALGORITHM

Arif Bourzami, Mohammed Amroune, Tarek Bouktir

Анотація


В последние годы проблема нестабильности напряжения привлекла особое внимание многих служб эксплуатации и исследователей. Настоящая статья посвящена оценке в режиме онлайн стабильности напряжения в энергосистеме с использованием адаптивной нейро-нечеткой системы вывода (ANFIS). Разработанная модель ANFIS принимает в качестве входных переменных величины напряжения и их фазы, полученные от шин в системе. Идентификация шин сформулирована как задача оптимизации, учитывающая эксплуатационные расходы, реальные потери мощности и показатель стабильности напряжения. Недавно разработанный алгоритм оптимизации методом мотылька и пламени (MFO) адаптирован для решения данной задачи оптимизации. Проверка предложенного подхода к онлайн оценке стабильности напряжения в сети проводилась на тестовых системах IEEE с 30 шинами и IEEE со 118 шинами. Полученные результаты показывают, что предлагаемый подход может обеспечить более высокую точность по сравнению с многоуровневыми нейронными сетями (MLP) и нейронными сетями с радиальными базисными функциями (RBF). 

Ключові слова


стабильность напряжения; показатель стабильности напряжения сети; оптимизация методом мотылька и пламени; адаптивная нейро-нечеткая система вывода

Повний текст:

PDF ENG (English)

Посилання


Modi P.K., Singh S.P., Sharma J.D. Voltage stability evaluation of power system with FACTS devices using fuzzy neural network. Engineering Applications of Artificial Intelligence, 2007, vol.20, no.4, pp. 481-491. doi: 10.1016/j.engappai.2006.08.003.

Larki F., Joorabian M., Meshgin Kelk H., Pishvaei M. Voltage Stability Evaluation of The Khouzestan Power System in Iran Using CPF Method and Modal Analysis. 2010 Asia-Pacific Power and Energy Engineering Conference. doi: 10.1109/appeec.2010.5448825.

Xue Y., Manjrekar M., Lin C., Tamayo M., Jiang J.N. Voltage stability and sensitivity analysis of grid-connected photovoltaic systems. 2011 IEEE Power and Energy Society General Meeting, Jul. 2011. doi: 10.1109/pes.2011.6039649.

Kojima T., Mori H. Development of nonlinear predictor with a set of predicted points for continuation power flow. Electrical Engineering in Japan, 2008, vol.163, no.4, pp. 30-41. doi: 10.1002/eej.20297.

Modarresi J., Gholipour E., Khodabakhshian A. A comprehensive review of the voltage stability indices. Renewable and Sustainable Energy Reviews, 2016, vol.63, pp. 1-12. doi: 10.1016/j.rser.2016.05.010.

Zhou D.Q., Annakkage U.D., Rajapakse A.D. Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network. IEEE Transactions on Power Systems, 2010, vol.25, no.3, pp. 1566-1574. doi: 10.1109/tpwrs.2009.2038059.

Chakrabarti S., Jeyasurya B. On-line voltage stability monitoring using artificial neural network. 2004 Large Engineering Systems Conference on Power Engineering (IEEE Cat. No.04EX819). doi: 10.1109/lescpe.2004.1356271.

Jayasankar V., Kamaraj N., Vanaja N. Estimation of voltage stability index for power system employing artificial neural network technique and TCSC placement. Neurocomputing, 2010, vol.73, no.16-18, pp. 3005-3011. doi: 10.1016/j.neucom.2010.07.006.

Ashraf S.M., Gupta A., Choudhary D.K., Chakrabarti S. Voltage stability monitoring of power systems using reduced network and artificial neural network. International Journal of Electrical Power & Energy Systems, 2017, vol.87, pp. 43-51. doi: 10.1016/j.ijepes.2016.11.008.

Chakraborty K., De A., Chakrabarti A. Voltage stability assessment in power network using self organizing feature map and radial basis function. Computers & Electrical Engineering, 2012, vol.38, no.4, pp. 819-826. doi: 10.1016/j.compeleceng.2012.03.012.

Devaraj D., Preetha Roselyn J. On-line voltage stability assessment using radial basis function network model with reduced input features. International Journal of Electrical Power & Energy Systems, 2011, vol.33, no.9, pp. 1550-1555. doi: 10.1016/j.ijepes.2011.06.008.

Moghavvemi M., Yang S.S. ANN Application Techniques for Power System Stability Estimation. Electric Machines & Power Systems, 2000, vol.28, no.2, pp. 167-178. doi: 10.1080/073135600268441.

Hashemi S., Aghamohammadi M.R. Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network. International Journal of Electrical Power & Energy Systems, 2013, vol.49, pp. 86-94. doi: 10.1016/j.ijepes.2012.12.019.

Bedoya D.B., Castro C.A., da Silva L.C.P. A method for computing minimum voltage stability margins of power systems. IET Generation, Transmission & Distribution, 2008, vol.2, no.5, p. 676. doi: 10.1049/iet-gtd:20070194.

Reddy M.J., Mohanta D.K. Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings. IET Generation, Transmission & Distribution, 2008, vol.2, no.2, p. 235. doi: 10.1049/iet-gtd:20070079.

Senthil Kumar A., Rajasekar S., Raj P.A.-D.-V. Power Quality Profile Enhancement of Utility Connected Microgrid System Using ANFIS-UPQC. Procedia Technology, 2015, vol.21, pp. 112-119. doi: 10.1016/j.protcy.2015.10.017.

Pappachen A., Fathima A.P. Load frequency control in deregulated power system integrated with SMES–TCPS combination using ANFIS controller. International Journal of Electrical Power & Energy Systems, 2016, vol.82, pp. 519-534. doi: 10.1016/j.ijepes.2016.04.032.

Sree Varshini G.Y., Raja S.C., Venkatesh P. Design of ANFIS Controller for Power System Stability Enhancement Using FACTS Device. Power Electronics and Renewable Energy Systems, 2014, pp. 1163-1171. doi: 10.1007/978-81-322-2119-7_113.

Yabe K., Koda J., Yoshida K., Chiang K.H., Khedkar P.S., Leonard D.J., Miller N.W. Conceptual designs of AI-based systems for local prediction of voltage collapse. IEEE Transactions on Power Systems, 1996, vol.11, no.1, pp. 137-145. doi: 10.1109/59.485995.

Berizzi A., Bovo C., Delfanti M., MerloM., Pozzi M. A Neuro-Fuzzy Inference System for the Evaluation of Voltage Collapse Risk Indices. Bulk Power System Dynamics and Control, 2004, pp. 22-27.

Torres S.P., Peralta W.H., Castro C.A.Power System Loading Margin Estimation Using a Neuro-Fuzzy Approach. IEEE Transactions on Power Systems, 2007, vol.22, no.4, pp. 1955-1964. doi: 10.1109/tpwrs.2007.907380.

Modi P.K., Singh S.P., Sharma J.D. Voltage stability evaluation of power system with FACTS devices using fuzzy neural network. Engineering Applications of Artificial Intelligence, 2007, vol.20, no.4, pp. 481-491. doi: 10.1016/j.engappai.2006.08.003.

Modi P.K., Singh S.P., Sharma J.D. Fuzzy neural network based voltage stability evaluation of power systems with SVC. Applied Soft Computing, 2008, vol.8, no.1, pp. 657-665. doi: 10.1016/j.asoc.2007.05.004.

Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 2015, vol.89, pp. 228-249. doi: 10.1016/j.knosys.2015.07.006.

Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 1993, vol.23, no.3, pp. 665-685. doi: 10.1109/21.256541.

Reddy M.J., Mohanta D.K. A Wavelet-neuro-fuzzy Combined Approach for Digital Relaying of Transmission Line Faults. Electric Power Components and Systems, 2007, vol.35, no.12, pp. 1385-1407. doi: 10.1080/15325000701426161.

Chiu S.L. Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems, 1994, vol.2, no.3, pp. 267-278. doi: 10.3233/IFS-1994-2306.

Chiu S. Method and software for extracting fuzzy classification rules by subtractive clustering. Proceedings of North American Fuzzy Information Processing, 1996, pp. 19-22. doi: 10.1109/nafips.1996.534778.

Alizadeh Mousavi O., Cherkaoui R. Investigation of P–V and V–Q based optimization methods for voltage and reactive power analysis. International Journal of Electrical Power & Energy Systems, 2014, vol.63, pp. 769-778. doi: 10.1016/j.ijepes.2014.06.060.

Milano F. Continuation Power Flow Analysis. In Power System Modeling and Scripting. Springer, Power Systems, 2010, pp. 103-130. doi: 10.1007/978-3-642-13669-6_5.

Moghavvemi M., Faruque M.O. Estimation of voltage collapse from local measurement of line power flow and bus voltages. PowerTech Budapest 1999. Abstract Records. (Cat. No.99EX376). doi: 10.1109/ptc.1999.826508.

Zimmerman R.D., Murillo-Sanchez C.E., Thomas R.J. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 2011, vol.26, no.1, pp. 12-19. doi: 10.1109/tpwrs.2010.2051168.

Chih-Wen Liu, Chen-Sung Chang, Mu-Chun Su. Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements. IEEE Transactions on Power Systems, 1998, vol.13, no.2, pp. 326-332. doi: 10.1109/59.667346.

Hong Y.-Y. Voltage stability indicator for identification of the weakest bus/area in power systems. IEE Proceedings –Generation, Transmission and Distribution, 1994, vol.141, no.4, p. 305. doi: 10.1049/ip-gtd:19949985.

Qin W., Zhang W., Wang P., Han X. Power system reliability based on voltage weakest bus identification. 2011 IEEE Power and Energy Society General Meeting, Jul. 2011. doi: 10.1109/pes.2011.6039270.

Amroune M., Bourzami A., Bouktir T. Weakest Buses Identification and Ranking in Large Power Transmission Network by Optimal Location of Reactive Power Supports. TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014, vol.12, no.10. doi: 10.11591/telkomnika.v12i10.6508.

Gopalakrishnan K., Ceylan H., Attoh-Okine N.O. (Eds.) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, Springer-Verlag BerlinHeidelberg, 2009. doi: 10.1007/978-3-642-04586-8.


Пристатейна бібліографія ГОСТ


  1. Modi P.K., Singh S.P., Sharma J.D. Voltage stability evaluation of power system with FACTS devices using fuzzy neural network. Engineering Applications of Artificial Intelligence, 2007, vol.20, no.4, pp. 481-491. doi: 10.1016/j.engappai.2006.08.003.
  2. Larki F., Joorabian M., Meshgin Kelk H., Pishvaei M. Voltage Stability Evaluation of The Khouzestan Power System in Iran Using CPF Method and Modal Analysis. 2010 Asia-Pacific Power and Energy Engineering Conference. doi: 10.1109/appeec.2010.5448825.
  3. Xue Y., Manjrekar M., Lin C., Tamayo M., Jiang J.N. Voltage stability and sensitivity analysis of grid-connected photovoltaic systems. 2011 IEEE Power and Energy Society General Meeting, Jul. 2011. doi: 10.1109/pes.2011.6039649.
  4. Kojima T., Mori H. Development of nonlinear predictor with a set of predicted points for continuation power flow. Electrical Engineering in Japan, 2008, vol.163, no.4, pp. 30-41. doi: 10.1002/eej.20297.
  5. Modarresi J., Gholipour E., Khodabakhshian A. A comprehensive review of the voltage stability indices. Renewable and Sustainable Energy Reviews, 2016, vol.63, pp. 1-12. doi: 10.1016/j.rser.2016.05.010.
  6. Zhou D.Q., Annakkage U.D., Rajapakse A.D. Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network. IEEE Transactions on Power Systems, 2010, vol.25, no.3, pp. 1566-1574. doi: 10.1109/tpwrs.2009.2038059.
  7. Chakrabarti S., Jeyasurya B. On-line voltage stability monitoring using artificial neural network. 2004 Large Engineering Systems Conference on Power Engineering (IEEE Cat. No.04EX819). doi: 10.1109/lescpe.2004.1356271.
  8. Jayasankar V., Kamaraj N., Vanaja N. Estimation of voltage stability index for power system employing artificial neural network technique and TCSC placement. Neurocomputing, 2010, vol.73, no.16-18, pp. 3005-3011. doi: 10.1016/j.neucom.2010.07.006.
  9. Ashraf S.M., Gupta A., Choudhary D.K., Chakrabarti S. Voltage stability monitoring of power systems using reduced network and artificial neural network. International Journal of Electrical Power & Energy Systems, 2017, vol.87, pp. 43-51. doi: 10.1016/j.ijepes.2016.11.008.
  10. Chakraborty K., De A., Chakrabarti A. Voltage stability assessment in power network using self organizing feature map and radial basis function. Computers & Electrical Engineering, 2012, vol.38, no.4, pp. 819-826. doi: 10.1016/j.compeleceng.2012.03.012.
  11. Devaraj D., Preetha Roselyn J. On-line voltage stability assessment using radial basis function network model with reduced input features. International Journal of Electrical Power & Energy Systems, 2011, vol.33, no.9, pp. 1550-1555. doi: 10.1016/j.ijepes.2011.06.008.
  12. Moghavvemi M., Yang S.S. ANN Application Techniques for Power System Stability Estimation. Electric Machines & Power Systems, 2000, vol.28, no.2, pp. 167-178. doi: 10.1080/073135600268441.
  13. Hashemi S., Aghamohammadi M.R. Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network. International Journal of Electrical Power & Energy Systems, 2013, vol.49, pp. 86-94. doi: 10.1016/j.ijepes.2012.12.019.
  14. Bedoya D.B., Castro C.A., da Silva L.C.P. A method for computing minimum voltage stability margins of power systems. IET Generation, Transmission & Distribution, 2008, vol.2, no.5, p. 676. doi: 10.1049/iet-gtd:20070194.
  15. Reddy M.J., Mohanta D.K. Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings. IET Generation, Transmission & Distribution, 2008, vol.2, no.2, p. 235. doi: 10.1049/iet-gtd:20070079.
  16. Senthil Kumar A., Rajasekar S., Raj P.A.-D.-V. Power Quality Profile Enhancement of Utility Connected Microgrid System Using ANFIS-UPQC. Procedia Technology, 2015, vol.21, pp. 112-119. doi: 10.1016/j.protcy.2015.10.017.
  17. Pappachen A., Fathima A.P. Load frequency control in deregulated power system integrated with SMES–TCPS combination using ANFIS controller. International Journal of Electrical Power & Energy Systems, 2016, vol.82, pp. 519-534. doi: 10.1016/j.ijepes.2016.04.032.
  18. Sree Varshini G.Y., Raja S.C., Venkatesh P. Design of ANFIS Controller for Power System Stability Enhancement Using FACTS Device. Power Electronics and Renewable Energy Systems, 2014, pp. 1163-1171. doi: 10.1007/978-81-322-2119-7_113.
  19. Yabe K., Koda J., Yoshida K., Chiang K.H., Khedkar P.S., Leonard D.J., Miller N.W. Conceptual designs of AI-based systems for local prediction of voltage collapse. IEEE Transactions on Power Systems, 1996, vol.11, no.1, pp. 137-145. doi: 10.1109/59.485995.
  20. Berizzi A., Bovo C., Delfanti M., MerloM., Pozzi M. A Neuro-Fuzzy Inference System for the Evaluation of Voltage Collapse Risk Indices. Bulk Power System Dynamics and Control, 2004, pp. 22-27.
  21. Torres S.P., Peralta W.H., Castro C.A.Power System Loading Margin Estimation Using a Neuro-Fuzzy Approach. IEEE Transactions on Power Systems, 2007, vol.22, no.4, pp. 1955-1964. doi: 10.1109/tpwrs.2007.907380.
  22. Modi P.K., Singh S.P., Sharma J.D. Voltage stability evaluation of power system with FACTS devices using fuzzy neural network. Engineering Applications of Artificial Intelligence, 2007, vol.20, no.4, pp. 481-491. doi: 10.1016/j.engappai.2006.08.003.
  23. Modi P.K., Singh S.P., Sharma J.D. Fuzzy neural network based voltage stability evaluation of power systems with SVC. Applied Soft Computing, 2008, vol.8, no.1, pp. 657-665. doi: 10.1016/j.asoc.2007.05.004.
  24. Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 2015, vol.89, pp. 228-249. doi: 10.1016/j.knosys.2015.07.006.
  25. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 1993, vol.23, no.3, pp. 665-685. doi: 10.1109/21.256541.
  26. Reddy M.J., Mohanta D.K. A Wavelet-neuro-fuzzy Combined Approach for Digital Relaying of Transmission Line Faults. Electric Power Components and Systems, 2007, vol.35, no.12, pp. 1385-1407. doi: 10.1080/15325000701426161.
  27. Chiu S.L. Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems, 1994, vol.2, no.3, pp. 267-278. doi: 10.3233/IFS-1994-2306.
  28. Chiu S. Method and software for extracting fuzzy classification rules by subtractive clustering. Proceedings of North American Fuzzy Information Processing, 1996, pp. 19-22. doi: 10.1109/nafips.1996.534778.
  29. Alizadeh Mousavi O., Cherkaoui R. Investigation of P–V and V–Q based optimization methods for voltage and reactive power analysis. International Journal of Electrical Power & Energy Systems, 2014, vol.63, pp. 769-778. doi: 10.1016/j.ijepes.2014.06.060.
  30. Milano F. Continuation Power Flow Analysis. In Power System Modeling and Scripting. Springer, Power Systems, 2010, pp. 103-130. doi: 10.1007/978-3-642-13669-6_5.
  31. Moghavvemi M., Faruque M.O. Estimation of voltage collapse from local measurement of line power flow and bus voltages. PowerTech Budapest 1999. Abstract Records. (Cat. No.99EX376). doi: 10.1109/ptc.1999.826508.
  32. Zimmerman R.D., Murillo-Sanchez C.E., Thomas R.J. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 2011, vol.26, no.1, pp. 12-19. doi: 10.1109/tpwrs.2010.2051168.
  33. Chih-Wen Liu, Chen-Sung Chang, Mu-Chun Su. Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements. IEEE Transactions on Power Systems, 1998, vol.13, no.2, pp. 326-332. doi: 10.1109/59.667346.
  34. Hong Y.-Y. Voltage stability indicator for identification of the weakest bus/area in power systems. IEE Proceedings –Generation, Transmission and Distribution, 1994, vol.141, no.4, p. 305. doi: 10.1049/ip-gtd:19949985.
  35. Qin W., Zhang W., Wang P., Han X. Power system reliability based on voltage weakest bus identification. 2011 IEEE Power and Energy Society General Meeting, Jul. 2011. doi: 10.1109/pes.2011.6039270.
  36. Amroune M., Bourzami A., Bouktir T. Weakest Buses Identification and Ranking in Large Power Transmission Network by Optimal Location of Reactive Power Supports. TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014, vol.12, no.10. doi: 10.11591/telkomnika.v12i10.6508.
  37. Gopalakrishnan K., Ceylan H., Attoh-Okine N.O. (Eds.) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, Springer-Verlag BerlinHeidelberg, 2009. doi: 10.1007/978-3-642-04586-8.




Copyright (c) 2019 Arif Bourzami, Mohammed Amroune, Tarek Bouktir


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ISSN 2074–272X (Print)
ІSSN 2309–3404 (Online)